Radial Basis Function Network based Design Optimization of Induction Motor

Author

Bellarmine, G. Thomas ; Bhuvaneswari, R. ; Subramanian, S.

Author_Institution

Florida A&M Univ., Tallahassee, FL

fYear

2005

fDate

March 31 2005-April 2 2005

Firstpage

75

Lastpage

80

Abstract

The application of radial basis function (RBF) network model for optimum design of induction motor (ODIM) is presented. The method utilizes simulated annealing (SA) technique to provide optimum design as training data to the RBF network. RBF is a new generation of artificial neural networks (ANN´s) of auto configuring nature and extremely fast training procedure. The RBF network model so developed is applied to a set of test data and results are compared with those obtained from the optimization technique (SA) results. Test results reveal that the proposed model determines the optimal dimensions of three phase induction motor along with the performance parameters efficiently and accurately

Keywords

electric machine CAD; induction motors; radial basis function networks; simulated annealing; RBF network; artificial neural networks; design optimization; optimum design of induction motor; radial basis function network; simulated annealing; three phase induction motor; Artificial neural networks; Convergence; DC motors; Design optimization; Induction motors; Neural networks; Radial basis function networks; Simulated annealing; Testing; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

SoutheastCon, 2006. Proceedings of the IEEE

Conference_Location

Memphis, TN

Print_ISBN

1-4244-0168-2

Type

conf

DOI

10.1109/second.2006.1629327

Filename

1629327